科技部補助專題研究計畫成果報告
期末報告
研究社會認知對團購網路使用者的行為干擾作用
計 畫 類 別 ： 個別型計畫 計 畫 編 號 ： MOST 102-2410-H-004-198- 執 行 期 間 ： 102 年 08 月 01 日至 104 年 02 月 28 日 執 行 單 位 ： 國立政治大學企業管理學系 計 畫 主 持 人 ： 洪叔民 計畫參與人員： 碩士班研究生-兼任助理人員：陳貞妮 碩士班研究生-兼任助理人員：劉榕安 報 告 附 件 ： 出席國際會議研究心得報告及發表論文 處 理 方 式 ： 1.公開資訊：本計畫涉及專利或其他智慧財產權，1 年後可公開查詢 2.「本研究」是否已有嚴重損及公共利益之發現：否 3.「本報告」是否建議提供政府單位施政參考：否中 華 民 國 104 年 05 月 31 日
中 文 摘 要 ： 本研究的主要目標是網路團購使用者的行為分析。在本研究 架構中，網路使用者行為將以三個構面代表，滿意度、信 任、以及購買意願。滿意度影響信任，信任導致購買意願。 滿意度又可分為對產品的滿意及對網路服務的滿意；信任可 分為對產品的信任及對網路服務的滿信任；網購意願可分為 對該網站的產品及對任何販售該產品的網路服務。另外，本 研究也探討社會認知對信任與團購意願關係的干擾作用。本 研究聘用一家市調公司並收集 300 份有效樣本。統計結果顯 示研究架構所有假設的因果關係皆顯著，社會認知的干擾作 用則在三個測試的關係中顯示兩個顯著的效果。 中文關鍵詞： 團購、社會認知，滿意度、信任、購買意願 英 文 摘 要 ： 英文關鍵詞：
A study for the moderating effect of social identify on user behavior in the
context of group buying
1. Introduction
The theory and practice of e-service is still in its infancy (Santos, 2003), but it indeed brings a
dramatic revolution to the internet world. An investigation regarding the B2C E-Commerce of USA
revealed double digit growth within the B2C e-commerce segment and estimated a turnover of more
than 170 million USD (yStats.com, 2011). It also reported that about 42% of internet users
purchased goods online, and will reach 230 million in 2011, accounting for more than 70% of total
population. The data showed that the differential types of e-commerce has infiltrated into people’s
daily life. Moreover, various researchers have recognized the E-service as a type of information
and media delivery service, and is slightly different from E-commerce (Voss et al. 2004).
Particularly, during the Internet boom, many dotcom firms offer the E-commerce service to
consumers via a variety of low-cost promotional mechanism. Most of the relative novelty of
E-commerce was free with limited functions at the beginning to attract the customers’ attention, and
then earned the profit later from the E-service indirectly or directly. According to the definition
(Rowley, 2006), E-service is deeds, efforts or performance whose delivery is mediated by
information technology (including the Web, information kiosks and mobile devices). Such E-service
includes the service element of e-tailing, customer support and service, and service delivery. On
the online customers expected (Zhao & Gutierrez, 2001). Therefore the websites with above
features have capability to detonate a revolutionary online consuming behavior, online Group
buying.
The plenty issue on the e-service market is the rise of the online Group buying behavior. The modus
of Group buying has embedding in many various business forms, including mass purchasing or
buying coupon, beyond the internet diffusing to the whole world. The evident circumstance around
is that the growth of E-service business is boosted by Group buying sites. A large number of Group
buying sites offer daily transactions and accumulate a considerable profit. These Group buying sites
show the power of offing discounts on various products and services due to a large number of
bargain-hunters involved in the shopping behavior. Furthermore, through these services, the
consumer could rapidly collect the real-time information of products and post feedback to the forum,
which results in enhancing the bargain power of consumers during the shopping procedure. These
Group buying sites accumulate a large number of orders in a short period of time to represent a
buyers’ collective bargaining power of coordinating with the suppliers for obtaining a desirable
price.
(Clark, 1997) had classified the E-commerce into six business models, the retail model, the mall
model, the subscriber model, the cable TV model, the arcade model, and the customization model.
the dedicated transaction site to sell the comprehensive category of products. Examples include
LivingSocial.com, Thrillist.com, Groupon.com, and AtCost.com. In addition, other firms focus on
the specific items or sections only such as food-addicted.com, wow-auction.com,
Babyhome.com.tw and so on.
The Group-buying websites operates across the industry at $1.1 billion last year, and estimates
gross revenues will grow 138 percent to $2.7 billion in 2011(Schonfeld, 2011). The famous online
Group buying site leader, Groupon.com, has made more than $100 million in merchandise sales in
2010, and expected to reach $760 million in revenue at the end of 2011 (Grant, 2010). Although
the stock price of Groupon has been dropped significantly for the past year and its revenues were
constantly below expectation for several quarters, it is still no doubt that online Group buying will
continue to be an important business model on Internet, and several issues remain unresolved
requiring further studies.
The following discuss the development of group buy sites in Taiwan. Atlaspost.com, one of the
leading social network sites (hereinafter referred to as SNS) in Taiwan announced recently that it’s
online social network will be shut down while the rest of the services, mainly providing discounts
for Group buying, remain operating (Atlaspost, 2011). When winning the People’s choice award
at 2008 DEMO, Atlaspost became the attention of media and attracted a lot of user registered as
other SNS, its major source of revenue is advertisements from sponsors (洪叔民, 2011). However,
this source of revenue is not enough for Atlaspost to sustain in the market. At the end of 2009, the
new policy of charging heavy users subscription fees was employed as an additional source of
revenue. Unfortunately, this strategy significantly altered the user behavior (Horng, 2011), and
still did not reach the target of a break-even. It is very likely that the management did not
successfully transform the user’s willingness of participating the virtual community to the
willingness of paying for subscriptions. After months of discussion and preparation, the
management of Atlaspost identified a new service of Group buying in which the number of
interested users exceeding certain limit will result in a special discount. This new service pushed
the site, already at the top spot of the traffic ranking in Taiwan, to an even higher position.
Ultimately, an international web company, Groupon, acquired Atlaspost in 2011 (Atlaspost, 2011).
Consequently, Atlaspost closed its social network service (Lo, 2012) and shifted its identity from
SNS to a channel providing Group buying service. Its rank in Taiwan dropped significantly from
the high rank of 29th recorded in the early 2011 and is now 96th according to Alexa1. However, it
remains one of the top Group buying and well-known web sites. Due to the characteristics of the
products, it usually classifies its items by location such as Taipei area and offers coupons for
restaurants or hotels. The user profile is not the key issue, while providing attracting products
drives the business running.
1
Babyhome.com.tw is another popular site in Taiwan, ranked 56th on Alexa2, providing Group
buying service. When Babyhome was established in 2002, it was simply a place for parents to
store the pictures and documents for their kids. However, as the visitors increased, it became a
social network site attracting many parents. The management of Babyhome took the opportunity
and developed the Group buying service targeting on products and services for babies and young
children. Because of its unique user profile, it has developed a different Group buying model that
targets parents as their main users and the products to be sold online are selected to satisfy the
demand of users. In some cases, its users even request a certain product through its social network
online. According to Digitime, the sales of Group buying service on Babyhome have surpassed
twenty-seven millions in 2008, and continue rising consequently.
Although both of Atlaspost and Babyhome provide Group buying services, a major difference is
observed representing two concepts of promoting online Group buying. Atlaspost provides a
larger variety of products/services to all of the customers interested in discounted prices, while
Babyhome targets on a certain type of customers, the parents, and offers a relatively less various
products/services. The social network or virtual community formed by parents on Babyhome is
critical to its Group buying model. Feedback and discussion topics on its virtual community
provide the guidance for the management to identify the proper products and services that were sold
successfully with a high confidence level on Babyhome. However, Groupon of Taiwan just closed
2
its social network service and its Group buying model relies on providing the most well-known and
efficient location for sellers and buyers to transact. The existence of virtual community that could
be theoretically represented by sense of belonging becomes the key factor differing Group from
Babyhome. Consumers are expected to behave differently on these two concepts of Group buying
model, and this study is intended to identify the differences and provide solutions to the following
questions:
- Consider the causal effects from satisfaction to trust that leads to the repurchase intention, what
are the differences between these two concepts of group buying models.
- How strong are the relationships between the trust toward the products or the sites and the
repurchase intention of any product traded on the site and the products traded in any online
service. How these two concepts differ on the relationships, trust toward the products to
purchase intention of any product traded on the site, trust toward the products to purchase
intention of the products traded on any site, and trust toward the sites to purchase intention of
any product traded on the site.
The results of this study could provide the guidelines of the strategies applied by the online Group
buying sites. For example, if the trust toward the site is positively correlated with the repurchase
intention of any product traded on the site, this online service could develop their brand name and
sell their products online or even offline. The e-book, Kindle by Amazon, signifies this scenario in
develop its own product. However, if this relationship is insignificant and the trust toward the
products is positively correlated with the repurchase intention of the products sold on any site, then
the online group buying service need to very carefully selecting the products or services traded on
its site. Its users will probably switch to the service providing the lowest prices for the product
they are interested in. The results of various combinations will imply different strategies that will
be very useful to the management of online group buying services.
2. Literature Review
Literature review will cover two topics, online paying behavior and group buying.
2.1 Online Paying Behavior
As the Internet has become increasingly popular and accepted as a facet of life for most people,
electronic channels provide an important avenue for companies to reach their customers and
generate sales (Moe and Fader, 2004). Many firms make their products or services available
online expecting to attract more customers. Understanding the purchasing behavior of online users
is an important topic for web service operators in managing their sites. Several papers focused on
this issue and provided useful information. Heijden et al. (2003) investigated two types of online
purchase intentions in shopping for CDs: technology-oriented and trust-oriented. Using students
as the samples, perceived risk and perceived ease of use were found directly to have influenced user
attitude toward purchasing intentions. A technology-oriented perspective, along with marketing
and psychological perspectives, was included in the framework of the study by Koufaris (2002).
survey regarding their purchase behavior on an online bookstore. His results showed that
shopping enjoyment and perceived usefulness strongly predicted the intention to return. However,
unplanned purchases were not affected by the factors in his model. Venkatesh and Agarwal (2006)
studied how visitors become customers in four different industries: airlines, online bookstores,
automobile manufacturers, and rental agencies. They collected longitudinal data and their results
show that the following factors had a significant effect on purchase behavior: time spent on the site,
content quality, made-for-the-medium purchase need, and previous purchase experience. Rather
than selling products or services, some web services charge service fees for the transactions made at
their sites. Black (2005) examined over three thousands eBay auction transactions and studied how
the likelihood to pay online is affected by consumer demographic, economic, and geographic
factors. The results pointed to several significant variables, including the value of transaction,
buyer gender, rural versus urban residence, and several characteristics of the community in which
the buyer lives.
Hume and Mort (2008) surveyed past and present performing arts audience members and identified
satisfaction as the antecedent of repurchase intention. The same concepts can be applied to
business to business context with similar results (Whittaker et al. 2007, Patterson and Spreng 1997).
When studying online purchase behavior, gender has moderated the effect of online concerns on
purchase likelihood (Janda 2008, Kahttab et al. 2012). Gender, along with age, educational level,
2011). Brown et al. (2003) classified online shoppers into six types, personalizing, recreational,
economic, involved, convenience-oriented and recreational, community-oriented, and apathetic and
convenience-oriented. Their results showed no relationship between shopping orientations and
online purchase intention, while product type, prior purchase and gender were significant factors.
Wells et al. (2011) proposed a model to investigate website quality as a potential signal of product
quality and consider the moderating effects of product information asymmetries and signal
credibility. The results indicate that website quality influences consumers’ perceptions of product
quality, which subsequently affects online purchase intentions. By surveying the users with online
shopping experience, the research of Samadi and Yaghoob-Nejadi (2009) revealed that consumers
perceived risk will decide the willingness of their future purchase intention.
In addition to products and services, virtual products such as music downloads are especially
suitable to be transacted online. Perceived usefulness, perceived enjoyment and perceived trust were
the strongest predictors of college students’ behavioral intentions to purchase online music
downloads (Bounagui and Nel 2009). Online gaming is another example and prior offline
purchase experience was found to be significantly related to paying for online games (Barbera et al.
2006). Although most users consider that the Internet should be free (Nielsen, 2010) and the
existence of a “free” mentality among online users was found and the significant factors of paying
for online content, clip arts, were linked to usage purpose (business versus personal) and experience
and Galletta (2006) studied users’ purchase behavior toward a different type of online content, i.e.,
intrinsically motivated online content. Examples can be found on sites featuring education, sports,
gossip, movie news, books, and adult material. The results showed that the variable of expected
benefits was the main antecedent for willingness to pay. Perceived quality and provider reputation
affected willingness to pay only indirectly through expected benefits. When considering the
Internet as a platform for interacting with others, word-of-mouth on the web has great impact on
consumers’ purchasing decisions (Riegner, 2007).
2.2 Online Group Buy
The concept of group buying is to collect and leverage the consumer’s bargaining power to obtain
the lower price or extra service of the products which they are interested in (Van Horn et al. 2000).
When a batched request is created by an internet-based platform, the Website claims that the
consumers will enjoy the lower price on their interested items, and the suppliers will benefit from
saving the cost of recruiting customers. It is a win-win situation between suppliers and consumers
(Kauffman et al. 2010).
The study on group buying is rare and most relevant literature focused on the issue of online auction
pricing mechanism (Chen et al. 2007, Chen et al. 2009) or comparing of externality effect, such as
price drop effect and ending effect (Kauffman & Wang, 2001, 2002). Some scholars interested in
programming the combinatorial coalition formation (Li et al. 2010), or working on comparing of the
al. 2007). Furthermore, the issue of quantity discount is also popular among three sub-dimensions,
price discrimination, channel coordination (Chen et al. 2009, Chen & Roma 2011), and operational
efficiency (Monahan, 1984). This coordination and negotiation process of price is a dynamic
mechanism, which can accumulate customers’ desired demand from different areas within a more
efficient and lower operation cost method (Kauffman & Wang, 2001). According to Anand &
Aron (2003), those people who are attracted to group buying sites are price sensitive. Even the
group buying market will have dominant only when a higher proportion of the consumers have the
lower willing to pay and the potential revenue decreases apparently, the suppliers would not gather
real advantage from it. On the other hand, the suppliers would leverage the demands and
word-of-mouth to catch up the positive effect of promotion and brand image (Kauffman & Wang,
2002).
Matsuo and Ito (2004) developed a system to support the formation of grouped buyers in order to
effectively expedite the transactions. By using the analytic hierarchy process, they proposed three
methods for group integrations, buyers trading in simple group buying, all buyers integrated, and
some buyers integrated. The strength of their system are that it can effectively express the buyers’
multiarttribute utilities in group integration and the buyers can purchase goods at a lower price.
Anand and Aron (2003) studies the online group buying from the perspective of price-discovery
mechanisms. Demand uncertainty was modeled as different conditions and the profits were
Enders et al. (2008) and Laudon & Traver (2008) leveraged the different revenue ways to classify
the e-commerce revenue models into three categorizations transaction model, advertising model,
and subscription model. The first one generates revenue by charging fees from people or companies
conducting transactions. In addition to products, the virtual goods with add value created by the
platform or the third-party goods or contents can be traded by individual users or companies. The
crucial driver for transaction model is charging the low transaction fees to have the mass of users
reaching the minimum level and having confidence in the service provided at this site. In contrast,
the number of transactions would be lower, if the platform operator charges higher fees in order to
accumulate the profits. To be profitable for running advertising model, a minimum number of
constant users is required and most of the revenue is collected by offering the opportunities of
advertising to the organizations. Consequently, a critical number of users and the minimum level of
trust are required for this model. The last model is subscription models which services are typically
free to users, but the heavy users will be willing to pay for the value-added information or contents.
Keeping its users loyal and satisfactory toward its services are critical to the success of the online
services. Most business model of group buying website works by negotiating deals with local
merchants and promising to deliver crowds in exchange for discounts. However, the group buying
sites are usually free to customers. The benefits are generated by charging vendors fees based on
the number of clicks on the site, and commissions per transaction. Traffic and purchasing rates thus
revenue hardly due to the required number of dealing discounts often becomes an uneasy achieved
threshold (Kauffman & Wang, 2002). In addition, Jughes and Beukes (2012) pointed out the risk of
the long-term value when investing group buying sites. They studied the two popular group
buying sites, Groupon and LivingSocial, and questioned particularly Groupon that its high traffic
might not result in return business. They urged the future investors to consider the long-term
value-creation and not the short-term payoff of these sites. The results of this study might provide
the solutions to the questions of their study.
3. Hypotheses Development
Fig. 1 Conceptual model describing the relationships between satisfaction, trust, and purchase intention
Fig. 1 illustrates the conceptual model of this research and the following section will describe the
development of the hypotheses.
Satisfaction Trust Purchase
Intention Satisfaction toward the product Trust toward the product The product traded in any web site Satisfaction toward the web
site
Trust toward the web site
Any product traded in this web site H1a H2a H1b H2b H3a H4 H3b Social Identity H5
Many researches have supported the positive influence of satisfaction on trust in the online context
(Ribbink et al. 2004). In the study of offline retail channels, consumer satisfaction is positively
related to future attitudes and behaviors (Webster and Sundaram 1998). However, most of the
work focused on the immediate intentions of behavior and less on the attitudinal variables such as
trust. Tax et al. (1998) conducted an investigation and found a significantly positive relationship
between satisfaction and trust. Therefore,
H1a: Satisfaction toward the product will positively influence the trust toward the product
The overall degree of pleasure felt by consumers in previous exchanges is an important antecedent
of consumer attitude. Young (2006) indicated that cognitive and experiential signals represent the
satisfaction level of a consumers shopping online. The influence of these signals on buyer trust
may be direct or indirect. It is reasonable to include the perception of the product as one of the
cognitive signals that include service quality, warranty, security and privacy policies indicated by
Martin et al. (2011). Thus,
H1b: Satisfaction toward the product will positively influence the trust toward the web site
Satisfaction and trust in online contexts are essential to maintain relationships with consumers.
The concept of satisfaction implies the fulfillment of expectations as well as a positive state based
on previous outcomes in the relationship with the website. Trust implies a willingness on the part
promises and will not exploit that vulnerability to its own benefit (Chouk and Perrien 2004).
Consumer trust in an internet purchase will have two dimensions: trust in the purchase site and in
the internet as a whole (Chen and Dhillon 1993). Since internet has becomes an important channel
for commodity trading, trust in the purchase site is a key factor for further study. Pavlou (2003)
linked consumer satisfaction to company trust. Satisfactory experiences with a specific online
retailer inspire consumer trust in the virtual medium and Santos and Fernandes (2011) provided
evidence that satisfied consumers in online purchasing will have a higher level of trust toward the
purchasing website. Based on the discussion,
H2a: Satisfaction toward the web site will positively influence the trust toward the web site
Signaling theory has been applied in an online context to investigate how traditional signals
influence trust and perceived risk with an online retailer (Aiken and Boush 2006). Wells et al. (2011)
applied signaling theory and proposed a hypothesis stating that the perceptions of website quality
positively correlated with a consumer’s perception of product quality. Many studies applied
theory of reasoned action (Ajzen and Fishbein 1980) to predict the online users’ behavior and
satisfaction toward the website usually represents the attitude in which website quality is an
important antecedent. In addition, the better perception of product quality will influence the trust
toward the product. The buyer’s overall satisfaction with the buying experience is proposed to
have a positive impact on the trust of the manufacturer. Hence,
The relationship between trust and purchase intention has been studied widely. Trust affects the
purchase intention through perceived risk and satisfaction in the study of Green and Pearson (2011).
The empirical finding of Ha et al. (2010) indicated that trust has directly affected the repurchase
intention. Perceived risk, an antecedent of trust was found significantly affect the intention to
shop online. Although the context of these researches is online shopping, the same concept can be
logically applied to product traded online. Therefore,
H3a: Trust toward the product will positively influence the purchase intention for the product
traded in any web site
The theory of reasoned action includes attitude as a key determinant of behavioral intention.
Gefen et al. (2003) indicated trust as a determinant of online purchase intention. The study by
Wells et al. (2011) indicated that the perceived quality of a product significantly affect a consumer’s
intention to use a website to purchase the product. Thus,
H3b: Trust toward the product will positively influence the purchase intention for any product
traded in this web site
Horng (2012) identified ten variables influencing the users’ online paying behavior and clustered
them into three factors. Of the most influential factor, the variable of security concerns has the
highest loading. In the context of online transaction, security concerns were often considered
relationship between trust toward the website and the repurchase intention on the site. Hence,
H4: Trust toward the web site will positively influence the purchase intention for any product
traded in this web site
The term “Web 2.0” originated in a series of conferences regarding new web technologies (O’Reilly,
2009) and has become a popular topic of investigation in recent years (Griffiths and Howard, 2008).
Three types of Web 2.0 collaborative tools are particularly important. They are Blogs, Mashups, and
Wikis (Dearstyne 2007). These tools have altered, and perhaps profoundly changed, the
development of internet-based social network sites. Although this concept can be found as early as
the 1960s in a computer-based education tool designed by University of Illinois Plato, commercial
interests arose only after the advent of the Internet, and increased significantly following the
development of Web 2.0. The Web 2.0 research platform and social-network approach offers
marketing research new tools to meet the challenges of the future (Cook and Buckley 2008). Social
applications are also appropriate in several areas for companies such as R&D, marketing, sales,
customer support, and operations (Bernoff and Li 2008). Although it is not clearly defined, SNS
can be considered equivalent to a virtual community in a broader sense (Hagel and Armstrong
1997). Three explanations as to why virtual communities play an important role in the Internet era
have been identified (Yoo and Donthu 2001). First, the virtual community has the potential to
become a new market for business. Second, the virtual community can be used to enhance customer
The importance of SNS can be signified by their ever increasing usage. These services enable users
to communicate and connect with each other, to build up a personal network with common interests,
allowing them to interact regularly in an organized way over the Internet. From this perspective,
social identity theory, proposing that the functionality of groups shapes their members’ social
identification with these groups (Dholakia et al. 2004), prescribes and instigates group-oriented
behavior. Pentina et al. (2008) suggested that stronger motivations to join virtual communities for
social interaction would lead to higher degrees of member identification with the group. Bergami
and Bagozzi (2000) found that social identity led to performance of organizational member
behavior by firm employees. In the study of Dholakia et al. (2004), a stronger social identity led
to higher levels of we-intentions, defined as the intentions to participate together as a group, to
participate in the virtual community. Based on the discussion,
H5: Social Identity will moderate the relationships between trust (toward the product or the web
site) and the repurchase intention (for any product traded in this web site or the product traded in
any web site).
4. Methodology and Results
To collect the online users’ behavior with experience of group buy, a marketing firm, Eastern Online, was hired to conduct the survey. A report by Ministry of Economic (2011) ranked the top 10 group buy web sites in Taiwan. However, the group buy market is very competitive and few of the web sites on this list were no longer available during the survey. With the help from Eastern Online, a new list, after removing 3 from the original list and adding 4 new sites, comprised the most popular
11 group buy web sites was used in the survey. The survey was conducted in March, 2015 and 300 effective samples were collected. The survey was administrated so that only effective samples were reported. Table 1 shows the descriptive statistics of the samples. Since the topic of this study is group buy, only the users with group purchase experience were surveyed. Therefore, most of the samples are married (55.7%), mid-aged (46.7% are older than 45), and experienced (more than 2 years of group buy experience 60.3%).
Table 1. Descriptive Statistics of Respondents’ Characteristics
Variables Category Frequency (n=577) Percentage (%)
Gender Male 152 50.7
Female 148 49.3 Marital Status Married 167 55.7 Single 133 44.3 Age <18 4 1.3 19 – 25 28 9.3 26 – 32 50 16.7 33 – 39 78 26.0 >45 140 46.7
Experience of Group Buy
< 6 month 27 9.0 6-12 months 29 9.7 12-18 months 35 11.7 18-24 months 28 9.3 >2 years 181 60.3
Device for Online Group Buy
Desk top 181 60.3 Laptop 80 26.7 Tablet 14 4.7 Mobile Phone 25 8.3 Monthly Budget <10000 86 28.7 10001-20000 89 29.7 20001-30000 48 16.0 30001-40000 23 7.7 >40001 54 18.0
Table 2. Operational Definition of Constructs
Construct References
Satisfaction toward the Website (WSat) Satisfaction toward the Product (PSat) Trust toward the Website (WTrust) Trust toward the Product (PTrust)
Purchase intention for any Product at the Website (WInt) Purchase intention for the Product at any Website (PInt) Social Identification (SOCI)
Table 2 provides the sources of the constructs in the present study. Where available, these constructs
were measured using questions adapted and revised from prior studies to enhance validity (Stone,
1978). All items were measured using seven-point Likert scales anchored from “strongly disagree”
comments from the participants provided a basis for questionnaire revisions. A pre-test conducted
by the same marketing firm collected 300 samples and the results were also used to revise the
questions.
Table 2. Summary of measure scales including factor loadings, average, and standard deviation of items, and Cronbach’s alpha, AVE of constructs
Factor loadings Item average Item standard Deviation Cronbach’s alpha/AVE 1 2 3 4 5 6 7 WSat1 .212 .237 .192 .755 .329 .168 .161 5.10 1.02 .895/.742 WSat2 .235 .272 .208 .745 .273 .196 .217 4.99 1.06 WSat3 .265 .279 .207 .698 .271 .314 .171 5.19 1.08 WSat4 .215 .313 .252 .730 .250 .272 .177 5.17 1.02 PSat1 .234 .252 .192 .373 .722 .200 .280 5.11 1.09 .915/.794 PSat2 .303 .228 .168 .419 .681 .229 .272 5.12 1.13 PSat3 .311 .215 .185 .383 .676 .237 .318 5.10 1.17 PSat4 .324 .256 .186 .372 .677 .213 .298 5.07 1.11 WTrust1 .178 .816 .212 .248 .168 .192 .206 5.21 1.12 .905/.786 WTrust2 .179 .826 .213 .255 .143 .205 .174 5.20 1.15 WTrust3 .173 .770 .249 .211 .197 .282 .205 5.23 1.17 WTrust4 .184 .766 .240 .226 .204 .320 .237 5.26 1.12 PTrust1 .289 .330 .248 .223 .299 .228 .680 5.22 1.13 .911/.792 PTrust2 .316 .294 .238 .246 .301 .245 .680 5.18 1.10 PTrust3 .323 .313 .253 .245 .338 .269 .642 5.16 1.16 PTrust4 .321 .329 .248 .248 .330 .251 .657 5.16 1.16 WInt1 .216 .359 .276 .297 .195 .702 .252 5.52 1.14 .916/.817 WInt2 .234 .359 .294 .280 .210 .717 .238 5.53 1.14 WInt3 .259 .339 .321 .266 .242 .668 .206 5.48 1.22 WInt4 .223 .383 .294 .280 .221 .704 .214 5.64 1.12 PInt1 .879 .165 .209 .187 .144 .159 .151 4.98 1.39 .916/.806 PInt2 .870 .179 .223 .181 .179 .134 .194 4.87 1.40 PInt3 .881 .138 .223 .178 .165 .124 .165 4.84 1.49 PInt4 .816 .161 .214 .181 .229 .199 .195 5.06 1.44 SocI1 .183 .216 .841 .173 .138 .223 .142 5.00 1.32 .902/.773 SocI2 .195 .207 .834 .164 .109 .211 .166 4.98 1.24 SocI3 .211 .163 .872 .122 .140 .148 .149 4.81 1.31 SocI4 .238 .183 .868 .184 .100 .121 .113 4.61 1.37 All of the loadings are significant at p < 0.01.
The reliability and validity of the data were tested and shown in Table 2. The items have
standardized loadings ranging from 0.658 to 0.879. According to the suggested guideline (Comrey
1973), all of the loadings are larger than 0.63 and are considered very good or excellent (larger than
measure relative to random measurement error. Estimates of Cronbach alpha above 0.7 and AVE
above 0.5 are considered supportive of internal consistency (Bagozzi and Yi 1988; Fornell and
Larcker 1981). The square root of the AVE for each construct is also compared and greater than the
correlation shared between the construct and other constructs in the model. Thus the discriminant
validity is verified.
Table 3 Results of the research model
Relationship Coefficients t-value p
H1a: PSat -> PTrust .738 12.09 .000
H1b: PSat -> WTrust .242 3.318 .001
H2a: WSat -> WTrust .551 7.537 .000
H2b: WSat -> PTrust .122 2.006 .046
H3a: PTrust -> Pint .697 12.626 .000
H3b: PTrust -> WInt .418 7.937 .000
H4: WTrust -> Wint .478 9.081 .000
***：α=0.01；**：α=0.05；*：α=0.1
The study used Partial Least Squares (PLS) to test the causal effects of the research model, and this
study applied SmartPLS 2.0 to analyse the structure model. Results in Table 3 indicate that all of
the hypotheses are tested significantly. Except H2b, the relationship between satisfaction toward
websites and trust toward products significant at 95 percent confidence level, all of the other
hypotheses are significant at 99 percent confident level. Both of the satisfaction toward websites
and products will have positive impact on trust of websites and products. The two cross
relationships, product satisfaction to website trust and website satisfaction to product trust, had
lower coefficients (.242 and .122) than those of two direct relationships, product satisfaction to
product trust and website satisfaction to website trust (.738 and .551). For the hypotheses regarding
intention of the products.
To test the moderating effects of social identification on the relationship between trust and purchase
intention, a multi-group analytical procedure (Keil, 2000) was used to compare the corresponding
path coefficients in the structural models. To generate two groups for social identification, 40
observations equal to the median value (5 in this case) were removed and the remaining
observations, 134 samples with measures smaller than 5 and 126 samples with measure larger than
5, were used to generate groups (Hair et al. 2010). According to the numbers shown in Table 4, the
t-values are -0.570, -3.565, and -2.855 for the moderating effects of social identification on the
relationships of trust toward products and purchase intention on products, trust toward product
and purchase intention on the websites, and trust toward websites and purchase intention on the
websites, respectively. Only the two relationships regarding purchase intention on the websites are
significantly moderated by social identification. These two relationships are stronger for low social
identification.
Table 4 Results for the moderating effect of social identification on purchase intention
Moderating effect of social identification on relationships Coefficient (t-value) t-value of Coefficient difference Results
PTrust -> PInt Low .807 (1.515***) -.570 Insignificant High .711 (5.314***)
PTrust -> PInt Low .807 (13.29***) -3.565*** Stronger for low social identification
High .454 (6.901***)
PTrust -> PInt Low .809 (13.30***) -2.855*** Stronger for low social identification
High .537 (9.130***) ***：α=0.01；**：α=0.05；*：α=0.1
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A Study of Poster and Viewer Participation in SNS
Shwu-Min Horng
Department of Business Administration, National Chengchi University, Taipei City, Taiwan (ROC)
Abstract - This article studies the individual and social
factors influencing the participation intention of viewers and posters in virtual communities. Reward, structural capital, and trust play a significant role for both types of user. Reciprocity has a positive impact only on the viewers' participation intention, while posters are also affected by reputation and cognitive capital. The positive relationships between cognitive capital and participant intention, and between trust and participation intention are stronger for posters than for viewers. In addition, managerial implications and suggestions for future research are discussed.
Keywords: Virtual communities, SNS, social capital,
user participation, viewers and posters
1 Introduction
The internet-based social network sites (hereinafter referred to as SNS) has drawn numerous attention from researchers as well as practitioners and its importance can be signified by their ever increasing usage [1]. SNSs enable users to communicate and connect with each other, to build up a personal network with common interests, allowing them to interact regularly in an organized way over the Internet. Although it is not clearly defined, SNS can be considered equivalent to a virtual community in a broader sense [2].
The key development with respect to the virtual community is the great increase of user-generated content on the Web, and the ability to easily search through it and combine parts of it to form new content. Therefore, encouraging users to provide content becomes an important issue for a given virtual community to attract more users and sustain its competiveness [3]. If providing content may be termed a posting activity, another activity of viewing, along with the posting activity, is made up of the fundamental elements in the ongoing life of any virtual community [4]. Viewing or lurking has not received much attention and few studies on these activities are to be found in a review of the literature, since most research tends to focus on active participants, that is, those who post online.
Researcher had controversial opinions toward viewing. Viewers were labeled as “free-riders” who drain the social
capital of the community because reading essentially means taking without giving back [5]. In contrast, another study presented lurking in a more positive light [6]. They discovered that many viewers considered themselves as community members, and were possessed of the characteristics that community members attribute to a successful online community [7].
Although viewers usually enjoy content on websites provided by others and do not actively participate in online communities, they account for the majority of users in many communities [8]. For online community sponsors and operators, viewers are important because they are part of the traffic, contributing to volume on servers, and respond to advertising and selling. It is important to understand how posters and viewers behave differently, so the online service operators can have strategies targeting their users more effectively.
2 Theoretical background
H1 H2 H3 H4 H5 H6 H7 Relational capital Cognitive capital Structural capital Individual factors Reputation Reward Identification Interaction Tenure Trust Reciprocity Intention to participate in a Virtual CommunityFig. 1 The research model for virtual community participation
Fig. 1 shows the research model for the hypotheses in this study. The factors governing the intention of viewers and posters to participate in virtual communities are tested and compared. In the following sections I will discuss
each of the constructs and their relationships to virtual community participation.
2.1 Individual motivations
As discussed previously, participation in virtual communities includes passively viewing and actively posting, which was modeled as contribution of knowledge [9]. In order to share knowledge with others, individuals must deem their contribution to be worth the effort, thereby generating new value. They also expect to receive some of that value for themselves [10]. The cost and benefit factors based on social exchange theory explain human behavior in social exchanges [11]. An interaction is considered as being rewarding if the benefit perceived by the subject is greater than the effort experienced. This implies that an individual can benefit from active participation if he/she perceives that participation enhances his/her personal reputation in the network. Although viewers do not interact with others and are considered free-riders [5], they are driven by the same factors affecting the posters who participate in SNS, if they are considered as the potential posters [12]. This inference leads to the first two sets of hypotheses. H1a: Reputation is positively associated with a higher level of participation by viewers.
H1b: Reputation is positively associated with a higher level of participation by posters.
Another important individual motivation is the reward provided by a web service to encourage user participation. Rewards are considered one of the extrinsic forms of motivation intended to increase participation in virtual communities [13]. In practices, various forms of rewards are implemented for both of viewers and posters to encourage users’ participation. Participation will be enhanced when the rewards of participating exceed the costs [14], which leads to the following hypotheses. H2a: Rewards are positively associated with a higher level of participation by viewers.
H2b: Rewards are positively associated with a higher level of participation by posters.
2.2 Social capital theory
Researchers have examined the role of social capital in the creation of intellectual capital, and proposed that social capital, i.e., the network of relationships possessed by an individual and the set of resources embedded within it, strongly influences the extent to which interpersonal knowledge sharing occurs [10]. Social capital was defined in terms of three diverse dimensions, 1) structural capital
represents links or connections between individuals; 2) the cognitive dimension focuses on the shared meaning and understanding that individuals or groups have with one another; and 3) relational capital refers to the personal relationships people have developed with each other through a history of interactions.
The connections between individuals, or the structural links created through the social interactions between individuals in a network, are important predictors of collective action [15]. This structural capital is the infrastructure of human capital that provides the environment which encourages individuals to create and leverage their knowledge by investing their human capital. Social interactions and ties were considered as channels for information and resource flows [16]. Individuals who occupy a central position within a collective have a relatively high proportion of direct ties to other members, and are likely to have developed the habit of cooperation. Moreover, such individuals are more likely to comply with group norms, which in turn lead to participation in a virtual community [17]. Although viewers are not the center of the virtual community, they could still build their connections with posters and other viewers through the center of the virtual groups. Thus, the following sets of hypotheses are:
H3a: Structural capital is positively associated with a higher level of participation by viewers.
H3b: Structural capital is positively associated with a higher level of participation by posters.
Cognitive capital represents the shared meaning and understanding that individuals or groups have with one another. Shared language and vocabulary influence the conditions for knowledge exchange in several ways [10]. As an individual interacts over time with others by sharing knowledge and learning the norms of practices in a virtual community, that individual develops his/her cognitive capital. Cognitive capital consists of mastering the application of expertise, which takes experience to build [9]. Individuals with a longer tenure in shared practice are likely to better understand how their expertise is relevant and may thus be more highly motivated to share knowledge as well as utilize a knowledge-sharing mechanism [18]. From the discussions above, it appears that the shared meaning and understanding within a virtual community could exist among both of viewers and posters. This leads to the following hypotheses:
H4a: Higher cognitive capital is positively associated with a higher level of participation by viewers.
H4b: Higher cognitive capital is positively associated with a higher level of participation by posters.
Trust is a key element in establishing long-term customer relations in virtual communities [19], and it is an important factor in conducting transactions online [18]. The value of virtual communities to sponsoring firms is dependent on the sponsor’s ability to cultivate trust with the members of communities, and provide theoretical contributions [20]. Since legal details cannot always be implemented, trust is an essential ingredient of long-term business engagements [21]. Furthermore, the lack of face-to-face contact in virtual communities increases the perceived risk of a relationship among users [22], and the risk of exposure of personal information. Users are constantly questioning the issue of security online, and trust is clearly the critical issue why posters or viewers continuously use the web services. Therefore I propose: H5a: Trust is positively associated with a higher level of participation by viewers.
H5b: Trust is positively associated with a higher level of participation by posters.
Reciprocity has been regarded as a benefit for individuals engaging in social exchange [23], or as the belief that current contributions will lead to future requests for knowledge being met [11]. Norm of reciprocity is a highly productive component of social capital, and the frequently cited reason for participation in virtual communities. In another research, reciprocity was defined as one of the perceived extrinsic sources of motivation that has a positive effect on individual’s use of a knowledge-sharing mechanism [13]. Although not clearly defined, the focuses of these literatures are posters who are motivated by reciprocity to share their knowledge online. However, viewers are potential posters and might be encouraged by the same reason. Therefore the following hypotheses have been developed accordingly: H6a: Norm of reciprocity is positively associated with a higher level of participation by viewers.
H6b: Norm of reciprocity is positively associated with a higher level of participation by posters.
Identification is the process by means of which individuals see themselves as one with another person or group of people [10], and is one of the internal motivations for participation [24]. In this study, users identify themselves as members of a community and align their goals with those of the community as a whole. Identification is also referred as an individual’s sense of belonging and a positive feeling toward a virtual community, which is similar to emotional attachment to the community [25]. Viewers and posters are both the members of the online communities. Thus, I hypothesize that both viewers and posters are motivated by their identity in a virtual community:
H7a: Identification is positively associated with a higher level of participation by viewers.
H7b: Identification is positively associated with a higher level of participation by posters.
3 Data collection
The data were collected from a website established in 2007. It was ranked in the top 20 among SNS in Taiwan [26]. A survey from the users of a single web service provides this study with a clear classification for the type of users, viewers and posters. A banner with a hyperlink connecting to a web survey was posted on its homepage. In order to collect enough data from posters, a mechanism was designed so that the banner would also be triggered when users are posting. The first page of the questionnaire explained the purpose of this survey and ensured confidentiality.
After excluding 165 invalid questionnaires for various reasons, a total of 364 valid samples were available for analysis, yielding an effective rate of 68.8 percent. The numbers of viewing and posting users are 220 and 144, respectively. No significant differences were found across the demographic information between viewers and posters upon visual inspection, and the profile of the sample basically matches expectations, with a higher proportion of male, student, and younger users.
4 Results and analysis
Multi-item, five-point Liker scale items were used to measure the constructs in the model. Scales were developed based on the review of the most relevant literature. To ensure that face validity, an iterative evaluation process was implemented so that each item used represented its definition without ambiguity [27]. To further validate the measurement model, convergent validity and discriminant validity were tested with results satisfying the requirements suggested. In sum, the results of content validity, convergent validity, and discriminant validity enable this study to proceed to estimations of the regression models.
For latent constructs where multiple items are available, they are combined into one indicator according to the partial disaggregation model [28]. In contrast to models where every item is a separate indicator, this yields models with fewer parameters to estimate, and better ratios of cases to parameters, while reducing measurement errors to a certain extent.
The intention to participate in a virtual community was tested for viewers and posters, respectively, using regression analysis. The results are shown in Table 1.
Table 1 Regression results
Viewers Poster
Stand. Coeff. Sig. Stand. Coeff. Sig.
H1 Reputation 1.433* 0.087 2.387** 0.011 H2 Reward 3.645*** 0.000 3.477*** 0.000 H3 Structural 2.191** 0.017 2.287** 0.021 H4 Cognitive -0.373 0.785 2.219** 0.032 H5 Trust 1.910** 0.039 3.057** 0.002 H6 Reciprocity 1.769* 0.068 1.883* 0.094 H7 Identification 0.488 0.874 0.356 0.748 R2 0.416 0.427 Adjusted R2 0.382 0.406 F 17.483*** 0.000 12.548*** 0.000 ***p < 0.01; **p < 0.05; *p < 0.1
Both of the regression models are significant at 0.01, and the differences between square and adjusted R-square indicates no over-fitting problem for the number of independent variables. Regression models for viewer and poster intentions explain 41.6 and 42.7 percent of the variance, respectively. Both of the R2 are significant at the 0.01 level. For viewers, H1a, H2a, H3a, H5a, and H6a are supported. Reputation, rewards, structural capital, trust, and reciprocity have a significantly positive effect on viewers’ intention to participate in the virtual community. H4a and H7a are not supported. Cognitive capital and identification capital have no significant relationship with the participation behavior of viewers. Variables showing significant effects on the posters’ intention are reputation, reward, structural capital, cognitive capital, trust, and reciprocity in which support hypotheses of H1b, H2b, H3b, H4b, H5b, and H6b. H7b is not supported indicating that identification has insignificant relationships with the posters’ intention to participate in the virtual community. Note that both viewers and posters behave in a similar way with respect to the variables of reputation, reward, structural capital, trust, reciprocity, and identification. Reputation, reward, structural capital, reciprocity, and trust are all positively significant while identification is insignificant. Cognitive capital has diverse results for viewers and posters.
5 Conclusions and discussion
The tests of the hypotheses are summarized in Table 2. H7 is the only hypothesis not supported for both of viewers and posters. The case company was established relatively recently, and it is very likely still at an early stage of development. Its users have not built a strong sense of belonging toward this virtual community. In addition, the identification construct is insignificant for both of viewers and posters. On the other hand, rewards are the most significant factor for both posters and viewers in their participating behavior, and it might serve as a
salient motivator for knowledge contributors when the identification factor is weak [11]. Various types of reward system are commonly observed for online services at their early development stage.
Table 2 Summarized results of the hypotheses tests Hypotheses Results
Viewer (a) Poster (b)
H1 Reputation is positively
associated with a higher level of participation
Supported Supported
H2 Rewards are positively
associated with a higher level of participation
Supported Supported
H3 Structural capital is
positively associated with a higher level of participation
Supported Supported
H4 Higher cognitive capital is
positively associated with a higher level of participation
Not supported
Supported
H5 Trust is positively
associated with a higher level of participation
Supported Supported
H6 Norm of reciprocity is
positively associated with a higher level of participation
Supported Supported
H7
Identification is positively associated with a higher level of participation
Not supported
Not supported
In addition to individual factors of reward and reputation, structural capital, trust, and norm of reciprocity are significant factors related to the intention to participate regardless of whether users are viewers or posters. Trust is the most important factor for a successful virtual community from the perspective of members as well as operators [29], and it is also positively related to the quality of knowledge sharing [25]. Trust is an antecedent factor for participating in virtual brand communities [22] and plays the same important role for any user to participate in a website on a continuous basis. For structural capital, the results are similar to those of another study [9], despite the slightly different measures applied. It is interesting to note that cognitive capital has inconsistent results for viewers and posters. Reputation and cognitive capital are significant predictors of participation for posters, consistent with prior research in online settings for knowledge contribution [9], but are insignificant for viewers. Viewers are not motivated by reputation, possibly because they are not easily identified by other users and many virtual communities, including funP, provide the mechanism to rank posters based on either their posting frequencies and/or the responses to their postings from other users. In general, users with a higher ranking are considered to have better reputations in that virtual community.
